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Abstract

A property is a bundled good composed of an appreciating asset, land, and a depreciating asset, structure. This study proposes the use of a hedonic based unobserved components approach where land and structure are viewed as two additive components of the price. The underlying trend in each component is identified via mapping each component’s intrinsic hedonic characteristics and using their different dynamic behaviour. The estimation approach uses a modified form of the Kalman filter. One advantage of the estimation strategy is that the composition of sales at any given period do not lead to abrupt or volatile changes in the estimates of the components. The algorithm is simple to implement and we demonstrate, using three datasets representing different urban settings in two countries, that the method produces land value predictions comparable to the state valuer’s assessments, and price indices comparable to recently published alternatives that rely on exogenous non-market information to disentangle land values. Our monthly indices are smoother than the quarterly counterparts.